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# Class to define the network architecture of the models
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.optim import Adam
class VanillaLSTM(nn.Module):
def __init__(
self, input_dim=1, hidden_dim=64, output_dim=1, num_layers=2, dropout=0.2
):
super(VanillaLSTM, self).__init__()
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.lstm = nn.LSTM(
input_size=input_dim,
hidden_size=hidden_dim,
num_layers=num_layers,
batch_first=True,
dropout=dropout,
)
self.fc = nn.Linear(in_features=hidden_dim, out_features=output_dim)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()
out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
out = self.fc(out[:, -1, :])
return out
class VAE(nn.Module):
def __init__(self, seq_len=48, n_features=1, hidden_dim=64, latent_dim=16, dropout=0.3):
super(VAE, self).__init__()
self.seq_len = seq_len
self.hidden_dim = hidden_dim
# Encoder
self.enc_lstm = nn.LSTM(
input_size=n_features,
hidden_size=hidden_dim,
batch_first=True
)
self.enc_dropout = nn.Dropout(p=dropout)
self.fc_mu = nn.Linear(hidden_dim, latent_dim)
self.fc_var = nn.Linear(hidden_dim, latent_dim)
# Decoder
self.fc_upsample = nn.Linear(latent_dim, seq_len * hidden_dim)
self.dec_dropout = nn.Dropout(p=dropout)
self.dec_lstm = nn.LSTM(
input_size=hidden_dim,
hidden_size=hidden_dim,
batch_first=True
)
self.fc_out = nn.Linear(hidden_dim, n_features)
def reparameterize(self, mu, log_var):
std = torch.exp(0.5 * log_var)
eps = torch.randn_like(std)
return mu + eps * std
def forward(self, x):
# Encode
_, (h_enc, c_enc) = self.enc_lstm(x)
h_enc = h_enc.squeeze(0) # shape: (batch_size, hidden_dim)
h_enc = self.enc_dropout(h_enc)
mu, log_var = self.fc_mu(h_enc), self.fc_var(h_enc)
# Reparameterize at latent space
z = self.reparameterize(mu, log_var)
# Decode
z = self.fc_upsample(z)
z = z.view(-1, self.seq_len, self.hidden_dim)
decoded, _ = self.dec_lstm(z)
dec_out = self.dec_dropout(decoded)
out = self.fc_out(dec_out)
return out, mu, log_var
class Transformer(nn.Module):
def __init__(self, input_dim=1, model_dim=64, num_layers=2, num_heads=4, dropout=0.2):
super(Transformer, self).__init__()
self.model_dim = model_dim
self.num_layers = num_layers
self.embedding = nn.Linear(input_dim, model_dim)
encoder_layer = nn.TransformerEncoderLayer(
d_model=model_dim,
nhead=num_heads,
dropout=dropout,
dim_feedforward=2*model_dim, # 128
batch_first=True
)
encoder_norm = nn.LayerNorm(model_dim)
self.transformer_encoder = nn.TransformerEncoder(
encoder_layer,
num_layers=num_layers,
norm=encoder_norm
)
decoder_layer = nn.TransformerDecoderLayer(
d_model=model_dim,
nhead=num_heads,
dropout=dropout,
dim_feedforward=2*model_dim, # 128
batch_first=True
)
decoder_norm = nn.LayerNorm(model_dim)
self.transformer_decoder = nn.TransformerDecoder(
decoder_layer,
num_layers=num_layers,
norm=decoder_norm
)
self.output = nn.Linear(model_dim, input_dim)
def forward(self, x):
embed_x = self.embedding(x)
enc_out = self.transformer_encoder(embed_x)
dec_out = self.transformer_decoder(embed_x, enc_out)
out = self.output(dec_out)
return out
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